class MultimodalFusion: """ Combines insights from image analysis and text analysis to provide comprehensive medical assessment """ def __init__(self): pass def fuse_insights(self, image_results, text_results): """ Fuse insights from image and text analysis Args: image_results (dict): Results from image analysis text_results (dict): Results from text analysis Returns: dict: Combined insights with recommendation """ # In a real implementation, this would use more sophisticated fusion techniques combined_insights = { "Image findings": image_results, "Text findings": text_results, } # Simple fusion logic confidence_scores = [ value for key, value in image_results.items() if isinstance(value, float) ] avg_confidence = ( sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0 ) # Determine if any abnormalities are detected in image image_abnormal = any( key != "No findings" and value > 0.5 for key, value in image_results.items() if isinstance(value, float) ) # Check if text analysis found concerning elements text_concerning = text_results.get("Sentiment") == "Concerning" # Generate recommendation if image_abnormal and text_concerning: recommendation = "High priority: Both image and text indicate abnormalities" elif image_abnormal: recommendation = "Medium priority: Image shows potential abnormalities" elif text_concerning: recommendation = "Medium priority: Text report indicates concerns" else: recommendation = "Low priority: No significant findings detected" combined_insights["Recommendation"] = recommendation combined_insights["Confidence"] = f"{avg_confidence:.2f}" return combined_insights